{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ablation_CAM",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYJfkOslruai",
        "colab_type": "text"
      },
      "source": [
        "# Importing All the required Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LK4JbCLV0j0g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from IPython.display import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "import glob\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import numpy as np\n",
        "import imageio as im\n",
        "from keras import models\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPool2D,BatchNormalization\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "import pandas as pd\n",
        "import keras.backend as K\n",
        "import cv2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4qrLLcUr2Zs",
        "colab_type": "text"
      },
      "source": [
        "Importing the CIFAR-10 Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTm0Px2r0vah",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train,y_train),(x_test,y_test)=keras.datasets.cifar10.load_data()\n",
        "y_train = keras.utils.to_categorical(y_train, 10)\n",
        "y_test = keras.utils.to_categorical(y_test, 10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjPCuWT-d1x8",
        "colab_type": "text"
      },
      "source": [
        "Defining the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jk2KztC-1EwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "outputId": "76798ff9-2814-481c-a1d7-f7857619581b"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(128, kernel_size=(3, 3),activation='relu',kernel_initializer='he_normal',input_shape=(32,32,3)))\n",
        "model.add(Conv2D(128, kernel_size=(3, 3),activation='relu',kernel_initializer='he_normal'))\n",
        "model.add(MaxPool2D((2, 2)))\n",
        "model.add(Dropout(0.20))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu',padding='same',kernel_initializer='he_normal'))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu',padding='same',kernel_initializer='he_normal'))\n",
        "model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu',padding='same',kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_5 (Conv2D)            (None, 30, 30, 128)       3584      \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 28, 28, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 14, 14, 64)        73792     \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 7, 7, 32)          18464     \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1568)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               200832    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 482,986\n",
            "Trainable params: 482,730\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwEhk2tm1sBI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=keras.optimizers.Adam(0.001),loss='categorical_crossentropy',metrics=['acc'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YE8KedrR2FN8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "2c96965c-13a5-41e3-e54c-3e04883ad1f0"
      },
      "source": [
        "model.fit(x_train,y_train,batch_size=32,epochs=10,validation_data=(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 0.7674 - acc: 0.7360 - val_loss: 0.6820 - val_acc: 0.7618\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.7200 - acc: 0.7525 - val_loss: 0.6525 - val_acc: 0.7788\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.6787 - acc: 0.7673 - val_loss: 0.6519 - val_acc: 0.7792\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.6426 - acc: 0.7783 - val_loss: 0.6199 - val_acc: 0.7925\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.6133 - acc: 0.7891 - val_loss: 0.6225 - val_acc: 0.7902\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 0.5904 - acc: 0.7959 - val_loss: 0.5939 - val_acc: 0.7986\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.5688 - acc: 0.8045 - val_loss: 0.5927 - val_acc: 0.8028\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.5482 - acc: 0.8121 - val_loss: 0.5903 - val_acc: 0.7961\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.5324 - acc: 0.8162 - val_loss: 0.5880 - val_acc: 0.8045\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 0.5105 - acc: 0.8239 - val_loss: 0.5942 - val_acc: 0.8003\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f31cc6f46d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xS17X5px3g0J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layer_outputs = [layer.output for layer in model.layers[:-5]] # Getting all the ouputs of layers (mainly Conv layers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfzVCffU3igC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "046d320a-c4c3-449d-e3b2-ccd5340251b2"
      },
      "source": [
        "layer_outputs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor 'conv2d_5/Relu:0' shape=(None, 30, 30, 128) dtype=float32>,\n",
              " <tf.Tensor 'conv2d_6/Relu:0' shape=(None, 28, 28, 128) dtype=float32>,\n",
              " <tf.Tensor 'max_pooling2d_2/MaxPool:0' shape=(None, 14, 14, 128) dtype=float32>,\n",
              " <tf.Tensor 'dropout_4/cond/Identity:0' shape=(None, 14, 14, 128) dtype=float32>,\n",
              " <tf.Tensor 'conv2d_7/Relu:0' shape=(None, 14, 14, 64) dtype=float32>,\n",
              " <tf.Tensor 'conv2d_8/Relu:0' shape=(None, 14, 14, 64) dtype=float32>,\n",
              " <tf.Tensor 'max_pooling2d_3/MaxPool:0' shape=(None, 7, 7, 64) dtype=float32>,\n",
              " <tf.Tensor 'dropout_5/cond/Identity:0' shape=(None, 7, 7, 64) dtype=float32>,\n",
              " <tf.Tensor 'conv2d_9/Relu:0' shape=(None, 7, 7, 32) dtype=float32>,\n",
              " <tf.Tensor 'dropout_6/cond/Identity:0' shape=(None, 7, 7, 32) dtype=float32>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUjJF5aG3vDf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "activation_model = models.Model(inputs=model.input, outputs=layer_outputs) # Activation model from Image to Last layer before Flatten"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZE6ekp3sv6i",
        "colab_type": "text"
      },
      "source": [
        "Importing a image of cifar-10 classes (Aeroplane)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBqa0srD36h-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "d035ce6d-ad8d-4519-ef43-bf1afd292a30"
      },
      "source": [
        "img = keras.preprocessing.image.load_img('a.jpg',target_size=(32,32,3))\n",
        "img = keras.preprocessing.image.img_to_array(img)\n",
        "array=keras.preprocessing.image.img_to_array(img)\n",
        "array=np.expand_dims(array,axis=0)\n",
        "img_tensor = array\n",
        "img_tensor /= 255.\n",
        "plt.imshow(img_tensor[0])\n",
        "plt.show()\n",
        "print(img_tensor.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(1, 32, 32, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZHqVQlq4mLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "18e6ea61-c7aa-469a-d0b1-93493232775f"
      },
      "source": [
        "model.predict(img_tensor)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.01994794, 0.00705175, 0.2063187 , 0.17225969, 0.26422638,\n",
              "        0.04592136, 0.22877358, 0.01912298, 0.02850043, 0.00787729]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwHkk0225Zvm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "3afc7423-1480-4c04-a11d-8a3e1043dee3"
      },
      "source": [
        "activations = activation_model.predict(img_tensor) \n",
        "last_layer_activation = activations[8]\n",
        "print(last_layer_activation.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 22 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f31cc672378> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "(1, 7, 7, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuwxEU2W7Ptz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "14203a2d-ebb0-44c0-9644-0d11dd289757"
      },
      "source": [
        "heatmap = np.mean(last_layer_activation, axis=-1)\n",
        "heatmap = np.maximum(heatmap, 0) / np.max(heatmap)\n",
        "plt.imshow(heatmap[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f31cc5e1198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfVNRRqsWoPM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "18ca7add-3af1-4853-8906-d507dad46757"
      },
      "source": [
        "heatmap=heatmap[0,:,:,]\n",
        "heatmap.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cePoKlOGtlnk",
        "colab_type": "text"
      },
      "source": [
        "Replacing all the 0 values with epsilon as it leads to mathematical errors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQmL9bMXaRdb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "eps = K.epsilon()\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    for k in range(len(last_layer_activation[0][i][j])):\n",
        "      if last_layer_activation[0][i][j][k]==0.:\n",
        "        last_layer_activation[0][i][j][k]=eps"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmbM9z_hcqYq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "23cff4fc-cfd9-43d4-e8e5-a8a37fed014f"
      },
      "source": [
        "last_layer_activation #final last layer activation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[1.00000001e-07, 1.00000001e-07, 1.75954956e+02, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [3.33854027e+01, 1.00000001e-07, 8.10349197e+01, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.38178345e+02, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 6.64463196e+01],\n",
              "         ...,\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.35166214e+02, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 6.06854858e+01],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.64828506e+02, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 4.79073944e+01],\n",
              "         [7.12294769e+01, 1.00000001e-07, 1.01570099e+02, ...,\n",
              "          8.25197983e+00, 1.00000001e-07, 3.17642956e+01]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 3.29737806e+00, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [4.34948578e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          5.39723091e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          7.02292175e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         ...,\n",
              "         [1.80344334e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.05584290e+02, 1.00000001e-07, 1.00000001e-07],\n",
              "         [8.79123077e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.22568985e+02, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.39820877e+02, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.78091522e+02, 1.00000001e-07, 1.00000001e-07]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [7.64217758e+01, 4.78997726e+01, 1.00000001e-07, ...,\n",
              "          1.03679497e+02, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.28046768e+02, 1.00000001e-07, 1.00000001e-07],\n",
              "         ...,\n",
              "         [8.05195618e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.94214722e+02, 1.00000001e-07, 1.00000001e-07],\n",
              "         [3.10696640e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          2.64767242e+02, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 9.30218315e+00, 1.00000001e-07, ...,\n",
              "          2.16702698e+02, 9.43707504e+01, 1.00000001e-07]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 5.70694351e+01, ...,\n",
              "          3.07987652e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.08833145e+02, 1.00000001e-07, 8.60939407e+01],\n",
              "         ...,\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.23758919e+02, ...,\n",
              "          7.01589661e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 7.30307541e+01, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 4.33580637e+00],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00231628e+02, ...,\n",
              "          5.17228508e+00, 4.16430740e+01, 1.00000001e-07]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.31350739e+02, ...,\n",
              "          2.00592709e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 5.41011848e+01, ...,\n",
              "          4.12559090e+01, 1.00000001e-07, 6.58129358e+00],\n",
              "         ...,\n",
              "         [1.00000001e-07, 1.00000001e-07, 3.65446167e+01, ...,\n",
              "          6.35417900e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          5.97834015e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [2.96648235e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.16424622e+02, 1.00000001e-07, 1.00000001e-07]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 4.41209793e+00, 1.00000001e-07, ...,\n",
              "          1.56372852e+01, 1.21602640e+01, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          8.96593323e+01, 6.26025200e+00, 1.00000001e-07],\n",
              "         ...,\n",
              "         [1.03425512e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          9.85811996e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [2.54845257e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          7.68050766e+01, 1.90530224e+01, 1.00000001e-07],\n",
              "         [1.68736591e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          9.16100235e+01, 1.40524721e+01, 1.00000001e-07]]]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IR9BjOuQt3Je",
        "colab_type": "text"
      },
      "source": [
        "Creating another Ablated image for the process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhnPlv795u0F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_final=np.zeros_like(img_tensor)\n",
        "activations_final = activation_model.predict(image_final) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woqrzW059v1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0cd450cb-731b-449f-8537-dc6fadfed062"
      },
      "source": [
        "activations_final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[[[0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          ...,\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809]],\n",
              " \n",
              "         [[0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          ...,\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809]],\n",
              " \n",
              "         [[0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          ...,\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          ...,\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809]],\n",
              " \n",
              "         [[0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          ...,\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809]],\n",
              " \n",
              "         [[0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          ...,\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809],\n",
              "          [0.6795971 , 0.06449035, 1.2069532 , ..., 0.1766163 ,\n",
              "           0.03976811, 0.07192809]]]], dtype=float32),\n",
              " array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32),\n",
              " array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32),\n",
              " array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.],\n",
              "          [0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32),\n",
              " array([[[[0.       , 0.       , 0.       , ..., 3.34688  , 0.       ,\n",
              "           1.707391 ],\n",
              "          [1.4924911, 0.       , 0.       , ..., 5.6226635, 0.       ,\n",
              "           0.       ],\n",
              "          [1.4924911, 0.       , 0.       , ..., 5.6226635, 0.       ,\n",
              "           0.       ],\n",
              "          ...,\n",
              "          [1.4924911, 0.       , 0.       , ..., 5.6226635, 0.       ,\n",
              "           0.       ],\n",
              "          [1.4924911, 0.       , 0.       , ..., 5.6226635, 0.       ,\n",
              "           0.       ],\n",
              "          [1.9606524, 0.       , 0.       , ..., 5.8473296, 0.       ,\n",
              "           0.       ]],\n",
              " \n",
              "         [[0.       , 0.       , 0.       , ..., 2.3978128, 0.       ,\n",
              "           1.7528799],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          ...,\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [3.778418 , 0.       , 0.       , ..., 5.869661 , 0.       ,\n",
              "           0.       ]],\n",
              " \n",
              "         [[0.       , 0.       , 0.       , ..., 2.3978128, 0.       ,\n",
              "           1.7528799],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          ...,\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [3.778418 , 0.       , 0.       , ..., 5.869661 , 0.       ,\n",
              "           0.       ]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0.       , 0.       , 0.       , ..., 2.3978128, 0.       ,\n",
              "           1.7528799],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          ...,\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [3.778418 , 0.       , 0.       , ..., 5.869661 , 0.       ,\n",
              "           0.       ]],\n",
              " \n",
              "         [[0.       , 0.       , 0.       , ..., 2.3978128, 0.       ,\n",
              "           1.7528799],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          ...,\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [2.7846832, 0.       , 0.       , ..., 4.48916  , 0.       ,\n",
              "           0.       ],\n",
              "          [3.778418 , 0.       , 0.       , ..., 5.869661 , 0.       ,\n",
              "           0.       ]],\n",
              " \n",
              "         [[0.4953097, 0.       , 0.       , ..., 1.642162 , 0.       ,\n",
              "           0.6480367],\n",
              "          [3.1878629, 0.       , 0.       , ..., 2.8946846, 0.       ,\n",
              "           0.       ],\n",
              "          [3.1878629, 0.       , 0.       , ..., 2.8946846, 0.       ,\n",
              "           0.       ],\n",
              "          ...,\n",
              "          [3.1878629, 0.       , 0.       , ..., 2.8946846, 0.       ,\n",
              "           0.       ],\n",
              "          [3.1878629, 0.       , 0.       , ..., 2.8946846, 0.       ,\n",
              "           0.       ],\n",
              "          [3.6729107, 0.3600062, 0.       , ..., 3.8463135, 0.       ,\n",
              "           0.       ]]]], dtype=float32),\n",
              " array([[[[ 0.        ,  0.        ,  0.24237841, ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  6.3446236 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        ,  6.3446236 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  9.17039   , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  5.1523533 , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.425682  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  5.8176775 , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  1.8620465 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.08805   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[ 0.        ,  0.        ,  1.8620465 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.08805   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  0.32543582, ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.859298  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        ,  8.859298  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.399783  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  5.787792  , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  3.240657  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  5.550761  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.281525  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  5.5649147 , ...,  0.        ,\n",
              "            0.        ,  0.        ]]]], dtype=float32),\n",
              " array([[[[ 0.        ,  0.        ,  0.24237841, ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.425682  , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  5.550761  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.399783  , ...,  0.        ,\n",
              "            0.        ,  0.        ]]]], dtype=float32),\n",
              " array([[[[ 0.        ,  0.        ,  0.24237841, ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        ,  8.221461  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.425682  , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  2.6569293 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 14.8262005 , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 17.41685   , ...,  0.        ,\n",
              "            0.        ,  0.        ]],\n",
              " \n",
              "         [[ 0.        ,  0.        ,  5.550761  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          ...,\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.215485  , ...,  0.        ,\n",
              "            0.        ,  0.        ],\n",
              "          [ 0.        ,  0.        , 10.399783  , ...,  0.        ,\n",
              "            0.        ,  0.        ]]]], dtype=float32),\n",
              " array([[[[0.0000000e+00, 0.0000000e+00, 6.4751678e+00, ...,\n",
              "           0.0000000e+00, 6.6980848e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 2.0849331e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 1.0767479e+01, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 2.0512983e+01],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 1.0767479e+01, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 2.0512983e+01],\n",
              "          [0.0000000e+00, 0.0000000e+00, 2.1422305e+00, ...,\n",
              "           6.7965298e+00, 0.0000000e+00, 3.5349434e+01],\n",
              "          [0.0000000e+00, 0.0000000e+00, 3.9941821e+00, ...,\n",
              "           1.3734107e+01, 2.9448557e+00, 1.3186090e+01]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 3.1308255e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.9150863e-01, 2.9733898e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.2149086e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.2149086e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.7941301e+01, 0.0000000e+00, 2.9042931e+01],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           3.0886404e+01, 3.3109522e+00, 0.0000000e+00]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 7.6993036e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 3.5342789e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.5193196e+01, 0.0000000e+00, 3.5934235e+01],\n",
              "          [0.0000000e+00, 6.8417788e-03, 0.0000000e+00, ...,\n",
              "           2.6190248e+01, 5.2903605e+00, 0.0000000e+00]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 7.6993036e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 3.5342789e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.5193196e+01, 0.0000000e+00, 3.5934235e+01],\n",
              "          [0.0000000e+00, 6.8417788e-03, 0.0000000e+00, ...,\n",
              "           2.6190248e+01, 5.2903605e+00, 0.0000000e+00]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 5.3847325e-01, 0.0000000e+00, ...,\n",
              "           3.3297706e-01, 2.4575504e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.6386909e+00, 0.0000000e+00, 2.7101109e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.6386909e+00, 0.0000000e+00, 2.7101109e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.8588240e+01, 0.0000000e+00, 3.4269131e+01],\n",
              "          [0.0000000e+00, 4.5316591e+00, 0.0000000e+00, ...,\n",
              "           2.9437069e+01, 0.0000000e+00, 0.0000000e+00]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 1.0889932e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 1.3849797e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           7.6124592e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           7.6124592e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.1034893e+01, 0.0000000e+00, 8.8108006e+00],\n",
              "          [0.0000000e+00, 7.6731200e+00, 0.0000000e+00, ...,\n",
              "           2.1399687e+01, 1.1479235e+00, 0.0000000e+00]]]], dtype=float32),\n",
              " array([[[[0.0000000e+00, 0.0000000e+00, 6.4751678e+00, ...,\n",
              "           0.0000000e+00, 6.6980848e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 2.0849331e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 1.0767479e+01, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 2.0512983e+01],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 1.0767479e+01, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 2.0512983e+01],\n",
              "          [0.0000000e+00, 0.0000000e+00, 2.1422305e+00, ...,\n",
              "           6.7965298e+00, 0.0000000e+00, 3.5349434e+01],\n",
              "          [0.0000000e+00, 0.0000000e+00, 3.9941821e+00, ...,\n",
              "           1.3734107e+01, 2.9448557e+00, 1.3186090e+01]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 3.1308255e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.9150863e-01, 2.9733898e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.2149086e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.2149086e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.7941301e+01, 0.0000000e+00, 2.9042931e+01],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           3.0886404e+01, 3.3109522e+00, 0.0000000e+00]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 7.6993036e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 3.5342789e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.5193196e+01, 0.0000000e+00, 3.5934235e+01],\n",
              "          [0.0000000e+00, 6.8417788e-03, 0.0000000e+00, ...,\n",
              "           2.6190248e+01, 5.2903605e+00, 0.0000000e+00]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 7.6993036e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 3.5342789e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           5.1227970e+00, 0.0000000e+00, 5.5561395e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.5193196e+01, 0.0000000e+00, 3.5934235e+01],\n",
              "          [0.0000000e+00, 6.8417788e-03, 0.0000000e+00, ...,\n",
              "           2.6190248e+01, 5.2903605e+00, 0.0000000e+00]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 5.3847325e-01, 0.0000000e+00, ...,\n",
              "           3.3297706e-01, 2.4575504e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.6386909e+00, 0.0000000e+00, 2.7101109e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           8.6386909e+00, 0.0000000e+00, 2.7101109e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.8588240e+01, 0.0000000e+00, 3.4269131e+01],\n",
              "          [0.0000000e+00, 4.5316591e+00, 0.0000000e+00, ...,\n",
              "           2.9437069e+01, 0.0000000e+00, 0.0000000e+00]],\n",
              " \n",
              "         [[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 1.0889932e+00, 0.0000000e+00, ...,\n",
              "           0.0000000e+00, 1.3849797e+01, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           7.6124592e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          ...,\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           7.6124592e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "          [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
              "           1.1034893e+01, 0.0000000e+00, 8.8108006e+00],\n",
              "          [0.0000000e+00, 7.6731200e+00, 0.0000000e+00, ...,\n",
              "           2.1399687e+01, 1.1479235e+00, 0.0000000e+00]]]], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3pJhlM-944l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ff452657-6f1e-4fea-fbb8-75dd9181aaae"
      },
      "source": [
        "last_layer_activation_final = activations_final[8]\n",
        "print(last_layer_activation_final.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 7, 7, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBlywqhwuEN0",
        "colab_type": "text"
      },
      "source": [
        "Activation layer of ablated image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwI0snKQ-PVE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "dea19b9c-79c9-49cd-a3f6-44bbbe177715"
      },
      "source": [
        "heatmap_final = np.mean(last_layer_activation_final, axis=-1)\n",
        "heatmap_final = np.maximum(heatmap_final, 0) / np.max(heatmap_final)\n",
        "plt.imshow(heatmap_final[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f31cc5a0630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAK/ElEQVR4nO3d/6uedR3H8dfLsy1PfpmmNsUNFTRJrDTWIAwpRZkp2g/9oKB9oZAgY2Eg1S/RH1DUDxEMtRS/DPELSKg5aGKCXzdnuk1L1sItbeaaOpnOo69+OJdxsq1znfvc13XfvPd8wGHnPvft/f6M+TzXfV/3OffHSQSgjkNGvQAAw0XUQDFEDRRD1EAxRA0Us6CLO120eDKTxx/ZxV3P6rRDXx/JXEl6dtdxI5t9MFuyeHT/5m9MHTqSuXtfeVPv7N7r/V3XSdSTxx+pz6++vIu7ntV9p983krmSdOpt3xnZ7IPZqovuH9nsda99YiRzH/r2nQe8joffQDFEDRRD1EAxRA0UQ9RAMUQNFEPUQDFEDRRD1EAxRA0UQ9RAMa2itr3S9gu2X7T9w64XBWBws0Zte0LSryRdJOkMSVfYPqPrhQEYTJsj9QpJLybZmmSfpDWSLut2WQAG1SbqEyW9NOPy9uZr/8X21bafsv3Uvtf3Dmt9AOZoaCfKkqxOsjzJ8kWLJ4d1twDmqE3UOyQtm3F5afM1AGOoTdRPSjrN9im2F0m6XNK93S4LwKBmfTujJFO2r5H0e0kTkm5MsqnzlQEYSKv3KEtyn6TRvfkXgNb4iTKgGKIGiiFqoBiiBoohaqAYogaKIWqgGKIGiiFqoJhOdr1cvHCvLl7ybBd3Pau/vrtnJHMl6aT73x3Z7IPZ2s99cmSzp94fzXEx2e8utpI4UgPlEDVQDFEDxRA1UAxRA8UQNVAMUQPFEDVQDFEDxRA1UAxRA8UQNVBMm10vb7S90/ZzfSwIwPy0OVL/VtLKjtcBYEhmjTrJw5J29bAWAEMwtOfUM7ey3fOvfcO6WwBz1MlWtocfvWhYdwtgjjj7DRRD1EAxbV7Sul3So5JOt73d9re6XxaAQbXZn/qKPhYCYDh4+A0UQ9RAMUQNFEPUQDFEDRRD1EAxRA0UQ9RAMUQNFNPJVrbHTLytrx/5ly7uelbvZnTfp6YmJ0Y2+2C24uhtI5u9Yfeykcy1c8DrOFIDxRA1UAxRA8UQNVAMUQPFEDVQDFEDxRA1UAxRA8UQNVAMUQPFEDVQTJv3/V5me53tzbY32V7Vx8IADKbNb2lNSfpBkg22j5C03vbaJJs7XhuAAbTZyvblJBuaz9+UtEXSiV0vDMBg5vSc2vbJks6W9Ph+rvvPVravvfb+cFYHYM5aR237cEl3Sfp+kjc+fP3MrWyPOYbzb8CotKrP9kJNB31rkru7XRKA+Whz9tuSbpC0JcnPu18SgPloc6Q+R9JVks6zvbH5+HLH6wIwoDZb2T4iyT2sBcAQcEYLKIaogWKIGiiGqIFiiBoohqiBYogaKIaogWKIGiiGqIFiiBoohqiBYogaKIaogWKIGiiGqIFiiBoohqiBYogaKIaogWKIGiiGqIFi2ryZ/6G2n7D9TLOV7U/7WBiAwbTZyvYdSecl2dNsv/OI7fuTPNbx2gAMoM2b+UfSnubiwuYjXS4KwODabpA3YXujpJ2S1iZhK1tgTLWKOsl7Sc6StFTSCttn7uc2bGULjIE51Zdkt6R1klZ2sxwA89Xm7Pdxto9qPp+UdIGk57teGIDBtDn7fYKkm2xPaPqbwB1JftftsgAMqs3Z7z9JOruHtQAYAs5oAcUQNVAMUQPFEDVQDFEDxRA1UAxRA8UQNVAMUQPFEDVQDFEDxRA1UAxRA8UQNVAMUQPFEDVQDFEDxRA1UAxRA8UQNVAMUQPFEDVQTOuom/20nrbNe34DY2wuR+pVkrZ0tRAAw9F218ulki6WdH23ywEwX22P1L+QdJ2kA+5Ry1a2wHhos0HeJZJ2Jln//27HVrbAeGhT3zmSLrW9TdIaSefZvqXTVQEY2KxRJ/lRkqVJTpZ0uaQ/JLmy85UBGAiPk4Fi2uxP/R9JHpL0UCcrATAUHKmBYogaKIaogWKIGiiGqIFiiBoohqiBYogaKIaogWKIGihmTj8m2tY7kbZOdXHPszt1QSd/pVbe/tjEyGYfzD5yyLsjm/2pxX8fydwnJvYd8DqO1EAxRA0UQ9RAMUQNFEPUQDFEDRRD1EAxRA0UQ9RAMUQNFEPUQDGtflC62Z3jTUnvSZpKsrzLRQEY3Fx+++FLSf7Z2UoADAUPv4Fi2kYdSQ/aXm/76v3dYOZWtrt3sZUtMCptH35/IckO2x+XtNb280kennmDJKslrZakMz69KENeJ4CWWh2pk+xo/twp6R5JK7pcFIDBtdl0/jDbR3zwuaQLJT3X9cIADKbNw+8lku6x/cHtb0vyQKerAjCwWaNOslXSZ3pYC4Ah4CUtoBiiBoohaqAYogaKIWqgGKIGiiFqoBiiBoohaqAYogaK6WTf123/WKKv/ezaLu56VhPvjO63Po+9+dGRzT6YrZm8cGSz3zp/z0jm7nr7yQNex5EaKIaogWKIGiiGqIFiiBoohqiBYogaKIaogWKIGiiGqIFiiBooplXUto+yfaft521vsf35rhcGYDBtf6Hjl5IeSPJV24skfbTDNQGYh1mjtr1Y0rmSviFJSfZJ2tftsgAMqs3D71MkvSrpN7aftn19s6fWf5m5le3U3reGvlAA7bSJeoGkz0r6dZKzJb0l6YcfvlGS1UmWJ1m+YPJ/mgfQkzZRb5e0PcnjzeU7NR05gDE0a9RJXpH0ku3Tmy+dL2lzp6sCMLC2Z7+/J+nW5sz3Vknf7G5JAOajVdRJNkpa3vFaAAwBP1EGFEPUQDFEDRRD1EAxRA0UQ9RAMUQNFEPUQDFEDRRD1EAxToa/9avtVyX9bcD//FhJ/xzicpjN7IqzT0py3P6u6CTq+bD9VJKR/Jw5s5ldYTYPv4FiiBooZhyjXs1sZjN7cGP3nBrA/IzjkRrAPBA1UMxYRW17pe0XbL9o+3/ehrjDuTfa3mn7ub5mzpi9zPY625ttb7K9qsfZh9p+wvYzzeyf9jV7xhommveT/13Pc7fZftb2RttP9Ty7022sxuY5te0JSX+WdIGm35b4SUlXJOn8nUttnytpj6Sbk5zZ9bwPzT5B0glJNtg+QtJ6SV/p6e9tSYcl2WN7oaRHJK1K8ljXs2es4VpNv//dkUku6XHuNknLk/T+wye2b5L0xyTXf7CNVZLdw7r/cTpSr5D0YpKtzdY+ayRd1sfgJA9L2tXHrP3MfjnJhubzNyVtkXRiT7OTZE9zcWHz0dt3edtLJV0s6fq+Zo7ajG2sbpCmt7EaZtDSeEV9oqSXZlzerp7+5x4Xtk+WdLakx///LYc6c8L2Rkk7Ja2dsWlDH34h6TpJ7/c48wOR9KDt9bav7nFuq22s5mOcoj6o2T5c0l2Svp/kjb7mJnkvyVmSlkpaYbuXpx+2L5G0M8n6PubtxxeSfFbSRZK+2zwF60OrbazmY5yi3iFp2YzLS5uvldc8n71L0q1J7h7FGpqHgOskrexp5DmSLm2e266RdJ7tW3qarSQ7mj93SrpH00//+tD5NlbjFPWTkk6zfUpz8uBySfeOeE2da05W3SBpS5Kf9zz7ONtHNZ9Pavok5fN9zE7yoyRLk5ys6X/rPyS5so/Ztg9rTkqqeeh7oaReXvnoYxurttvudC7JlO1rJP1e0oSkG5Ns6mO27dslfVHSsba3S/pJkhv6mK3pI9ZVkp5tnttK0o+T3NfD7BMk3dS88nCIpDuS9PrS0ogskXTP9PdTLZB0W5IHepzf6TZWY/OSFoDhGKeH3wCGgKiBYogaKIaogWKIGiiGqIFiiBoo5t91pee3gcF+RQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snlK3kBy-eEy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cb8da007-473d-4e85-88d1-abf2752b57ba"
      },
      "source": [
        "print(last_layer_activation.shape,last_layer_activation_final.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 7, 7, 32) (1, 7, 7, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tn-1UTvmuKze",
        "colab_type": "text"
      },
      "source": [
        "Calculating Weight ratio according to research paper"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bxm9HCLW-1A7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weights_ratio=(last_layer_activation-last_layer_activation_final)/last_layer_activation"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pfo-sagM_Bfj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7dbb1884-0a58-41b1-874c-bdc86183a67a"
      },
      "source": [
        "weights_ratio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 1.0000000e+00,  1.0000000e+00,  9.6319979e-01, ...,\n",
              "           1.0000000e+00, -6.6980848e+07,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00, -2.0849331e+08,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  9.2207551e-01, ...,\n",
              "           1.0000000e+00,  1.0000000e+00,  6.9128484e-01],\n",
              "         ...,\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  9.2033899e-01, ...,\n",
              "           1.0000000e+00,  1.0000000e+00,  6.6197872e-01],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  9.8700333e-01, ...,\n",
              "          -6.7965296e+07,  1.0000000e+00,  2.6212990e-01],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  9.6067566e-01, ...,\n",
              "          -6.6434085e-01, -2.9448556e+07,  5.8487695e-01]],\n",
              "\n",
              "        [[ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00, -3.1308254e+07,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.8904055e-01, -2.9733898e+08,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           8.8302720e-01,  1.0000000e+00,  1.0000000e+00],\n",
              "         ...,\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.2219579e-01,  1.0000000e+00,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           8.5362285e-01,  1.0000000e+00, -2.9042931e+08],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           8.2657009e-01, -3.3109522e+07,  1.0000000e+00]],\n",
              "\n",
              "        [[ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00, -7.6993032e+07,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00, -3.5342787e+08,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.5999277e-01,  1.0000000e+00, -5.5561396e+07],\n",
              "         ...,\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.7362298e-01,  1.0000000e+00, -5.5561396e+07],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.4261682e-01,  1.0000000e+00, -3.5934234e+08],\n",
              "         [ 1.0000000e+00,  9.9926454e-01,  1.0000000e+00, ...,\n",
              "           8.7914205e-01,  9.4394070e-01,  1.0000000e+00]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00, -7.6993032e+07,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00, -3.5342787e+08,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.5292985e-01,  1.0000000e+00,  9.3546426e-01],\n",
              "         ...,\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.2698306e-01,  1.0000000e+00, -5.5561396e+07],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "          -1.5193197e+08,  1.0000000e+00, -7.2877860e+00],\n",
              "         [ 1.0000000e+00, -6.8416789e+04,  1.0000000e+00, ...,\n",
              "          -4.0635738e+00,  8.7295943e-01,  1.0000000e+00]],\n",
              "\n",
              "        [[ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00,  1.0000000e+00,  1.0000000e+00],\n",
              "         [ 1.0000000e+00, -5.3847310e+06,  1.0000000e+00, ...,\n",
              "           9.8340034e-01, -2.4575504e+08,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           7.9060715e-01,  1.0000000e+00,  5.8820999e-01],\n",
              "         ...,\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           8.6404711e-01,  1.0000000e+00, -2.7101108e+07],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           6.8907356e-01,  1.0000000e+00, -3.4269130e+08],\n",
              "         [ 1.0000000e+00, -4.5316592e+07,  1.0000000e+00, ...,\n",
              "           7.4715769e-01,  1.0000000e+00,  1.0000000e+00]],\n",
              "\n",
              "        [[ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           1.0000000e+00,  1.0000000e+00,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  7.5318021e-01,  1.0000000e+00, ...,\n",
              "           1.0000000e+00, -1.3893886e-01,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.1509575e-01,  1.0000000e+00,  1.0000000e+00],\n",
              "         ...,\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           9.2277980e-01,  1.0000000e+00,  1.0000000e+00],\n",
              "         [ 1.0000000e+00,  1.0000000e+00,  1.0000000e+00, ...,\n",
              "           8.5632604e-01,  1.0000000e+00, -8.8108008e+07],\n",
              "         [ 1.0000000e+00, -7.6731200e+07,  1.0000000e+00, ...,\n",
              "           7.6640451e-01,  9.1831166e-01,  1.0000000e+00]]]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Wsx5z-s_GOo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Multipling the weight ratio with final activation layer\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    last_layer_activation[0][i][j]=(last_layer_activation[0][i][j]*weights_ratio[0][i][j])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RhaSmhTcurbp",
        "colab_type": "text"
      },
      "source": [
        "Applying RELU to the Final activation layer ( product of weight ratio and activation layer)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdMWZrEed3hE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "56b2bc50-2036-40a7-f2f3-15b50fc6308a"
      },
      "source": [
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    for k in range(len(last_layer_activation[0][i][j])):\n",
        "      if last_layer_activation[0][i][j][k]<0.:\n",
        "        last_layer_activation[0][i][j][k]=0.\n",
        "last_layer_activation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[1.00000001e-07, 1.00000001e-07, 1.69479782e+02, ...,\n",
              "          1.00000001e-07, 0.00000000e+00, 1.00000001e-07],\n",
              "         [3.33854027e+01, 1.00000001e-07, 8.10349197e+01, ...,\n",
              "          1.00000001e-07, 0.00000000e+00, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.27410866e+02, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 4.59333344e+01],\n",
              "         ...,\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.24398735e+02, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 4.01725006e+01],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.62686279e+02, ...,\n",
              "          0.00000000e+00, 1.00000001e-07, 1.25579605e+01],\n",
              "         [7.12294769e+01, 1.00000001e-07, 9.75759201e+01, ...,\n",
              "          0.00000000e+00, 0.00000000e+00, 1.85782051e+01]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 3.29737806e+00, ...,\n",
              "          1.00000001e-07, 0.00000000e+00, 1.00000001e-07],\n",
              "         [4.34948578e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          5.33808022e+01, 0.00000000e+00, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          6.20143089e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         ...,\n",
              "         [1.80344334e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          9.73693848e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [8.79123077e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.04627686e+02, 1.00000001e-07, 0.00000000e+00],\n",
              "         [1.39820877e+02, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.47205124e+02, 0.00000000e+00, 1.00000001e-07]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 0.00000000e+00, 1.00000001e-07],\n",
              "         [7.64217758e+01, 4.78997726e+01, 1.00000001e-07, ...,\n",
              "          1.03679497e+02, 0.00000000e+00, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.22923973e+02, 1.00000001e-07, 0.00000000e+00],\n",
              "         ...,\n",
              "         [8.05195618e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.89091919e+02, 1.00000001e-07, 0.00000000e+00],\n",
              "         [3.10696640e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          2.49574051e+02, 1.00000001e-07, 0.00000000e+00],\n",
              "         [1.00000001e-07, 9.29534149e+00, 1.00000001e-07, ...,\n",
              "          1.90512451e+02, 8.90803909e+01, 1.00000001e-07]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 0.00000000e+00, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 5.70694351e+01, ...,\n",
              "          3.07987652e+01, 0.00000000e+00, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.03710350e+02, 1.00000001e-07, 8.05378036e+01],\n",
              "         ...,\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.23758919e+02, ...,\n",
              "          6.50361710e+01, 1.00000001e-07, 0.00000000e+00],\n",
              "         [1.00000001e-07, 1.00000001e-07, 7.30307541e+01, ...,\n",
              "          0.00000000e+00, 1.00000001e-07, 0.00000000e+00],\n",
              "         [1.00000001e-07, 0.00000000e+00, 1.00231628e+02, ...,\n",
              "          0.00000000e+00, 3.63527145e+01, 1.00000001e-07]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 0.00000000e+00, 1.31350739e+02, ...,\n",
              "          1.97262936e+01, 0.00000000e+00, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 5.41011848e+01, ...,\n",
              "          3.26172180e+01, 1.00000001e-07, 3.87118268e+00],\n",
              "         ...,\n",
              "         [1.00000001e-07, 1.00000001e-07, 3.65446167e+01, ...,\n",
              "          5.49030991e+01, 1.00000001e-07, 0.00000000e+00],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          4.11951599e+01, 1.00000001e-07, 0.00000000e+00],\n",
              "         [2.96648235e+01, 0.00000000e+00, 1.00000001e-07, ...,\n",
              "          8.69875488e+01, 1.00000001e-07, 1.00000001e-07]],\n",
              "\n",
              "        [[1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          1.00000001e-07, 1.00000001e-07, 1.00000001e-07],\n",
              "         [1.00000001e-07, 3.32310486e+00, 1.00000001e-07, ...,\n",
              "          1.56372852e+01, 0.00000000e+00, 1.00000001e-07],\n",
              "         [1.00000001e-07, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          8.20468750e+01, 6.26025200e+00, 1.00000001e-07],\n",
              "         ...,\n",
              "         [1.03425512e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          9.09687424e+01, 1.00000001e-07, 1.00000001e-07],\n",
              "         [2.54845257e+01, 1.00000001e-07, 1.00000001e-07, ...,\n",
              "          6.57701874e+01, 1.90530224e+01, 0.00000000e+00],\n",
              "         [1.68736591e+01, 0.00000000e+00, 1.00000001e-07, ...,\n",
              "          7.02103348e+01, 1.29045486e+01, 1.00000001e-07]]]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9iMx0c4u9N8",
        "colab_type": "text"
      },
      "source": [
        "Heatmap of final activation layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOvn9ytyeVxM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "862cefde-8955-46d5-c39d-d5f3f4c79054"
      },
      "source": [
        "heatmap = np.mean(last_layer_activation, axis=-1)\n",
        "heatmap = np.maximum(heatmap, 0) / np.max(heatmap)\n",
        "plt.imshow(heatmap[0])\n",
        "plt.savefig(\"heatmap.jpg\")\n",
        "heatmap=heatmap[0,:,:,]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-t35-jfvBcg",
        "colab_type": "text"
      },
      "source": [
        "# Super imposed image of original and heatmap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1Z_rXQThPBQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        },
        "outputId": "190663bc-3e13-4f6d-bc39-b7032bd9e550"
      },
      "source": [
        "img = keras.preprocessing.image.load_img('a.jpg')\n",
        "img = keras.preprocessing.image.img_to_array(img)\n",
        "heatmap = np.uint8(255 * heatmap)\n",
        "jet = cm.get_cmap(\"jet\")\n",
        "jet_colors = jet(np.arange(256))[:, :3]\n",
        "jet_heatmap = jet_colors[heatmap]\n",
        "jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)\n",
        "jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n",
        "jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)\n",
        "superimposed_img = jet_heatmap * 0.7 + img\n",
        "superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)\n",
        "save_path = \"final_image_aeroplane.jpg\"\n",
        "superimposed_img.save(save_path)\n",
        "display(Image(save_path))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApobD7WHvIbP",
        "colab_type": "text"
      },
      "source": [
        "This shows that the conv layer mainly focuses on the wings and the turbine of aeroplane for its classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odsS4jAaT75H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3c847e06-192c-4bde-8b8c-0893c0db3829"
      },
      "source": [
        "img = keras.preprocessing.image.load_img('b.jpg',target_size=(32,32,3))\n",
        "img = keras.preprocessing.image.img_to_array(img)\n",
        "array=keras.preprocessing.image.img_to_array(img)\n",
        "array=np.expand_dims(array,axis=0)\n",
        "img_tensor = array\n",
        "img_tensor /= 255.\n",
        "plt.imshow(img_tensor[0])\n",
        "plt.show()\n",
        "print(img_tensor.shape)\n",
        "\n",
        "model.predict(img_tensor)\n",
        "\n",
        "activations = activation_model.predict(img_tensor) \n",
        "last_layer_activation = activations[8]\n",
        "print(last_layer_activation.shape)\n",
        "\n",
        "heatmap = np.mean(last_layer_activation, axis=-1)\n",
        "heatmap = np.maximum(heatmap, 0) / np.max(heatmap)\n",
        "plt.imshow(heatmap[0])\n",
        "heatmap=heatmap[0,:,:,]\n",
        "\n",
        "eps = K.epsilon()\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    for k in range(len(last_layer_activation[0][i][j])):\n",
        "      if last_layer_activation[0][i][j][k]==0.:\n",
        "        last_layer_activation[0][i][j][k]=eps\n",
        "\n",
        "image_final=np.zeros_like(img_tensor)\n",
        "activations_final = activation_model.predict(image_final) \n",
        "\n",
        "last_layer_activation_final = activations_final[8]\n",
        "\n",
        "heatmap_final = np.mean(last_layer_activation_final, axis=-1)\n",
        "heatmap_final = np.maximum(heatmap_final, 0) / np.max(heatmap_final)\n",
        "plt.imshow(heatmap_final[0])\n",
        "\n",
        "weights_ratio=(last_layer_activation-last_layer_activation_final)/last_layer_activation\n",
        "\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    last_layer_activation[0][i][j]=(last_layer_activation[0][i][j]*weights_ratio[0][i][j])\n",
        "\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    for k in range(len(last_layer_activation[0][i][j])):\n",
        "      if last_layer_activation[0][i][j][k]<0.:\n",
        "        last_layer_activation[0][i][j][k]=0.\n",
        "\n",
        "heatmap = np.mean(last_layer_activation, axis=-1)\n",
        "heatmap = np.maximum(heatmap, 0) / np.max(heatmap)\n",
        "plt.imshow(heatmap[0])\n",
        "plt.savefig(\"heatmap_dog.jpg\")\n",
        "heatmap=heatmap[0,:,:,]\n",
        "\n",
        "img = keras.preprocessing.image.load_img('b.jpg')\n",
        "img = keras.preprocessing.image.img_to_array(img)\n",
        "heatmap = np.uint8(255 * heatmap)\n",
        "jet = cm.get_cmap(\"jet\")\n",
        "jet_colors = jet(np.arange(256))[:, :3]\n",
        "jet_heatmap = jet_colors[heatmap]\n",
        "jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)\n",
        "jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n",
        "jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)\n",
        "superimposed_img = jet_heatmap * 0.7 + img\n",
        "superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)\n",
        "save_path = \"final_image_dog.jpg\"\n",
        "superimposed_img.save(save_path)\n",
        "display(Image(save_path))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(1, 32, 32, 3)\n",
            "(1, 7, 7, 32)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtKAnA0uW-Sx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "7b6bc538-d9df-4cd9-ab7c-8fef7a2f3bdb"
      },
      "source": [
        "img = keras.preprocessing.image.load_img('c.jpeg',target_size=(32,32,3))\n",
        "img = keras.preprocessing.image.img_to_array(img)\n",
        "array=keras.preprocessing.image.img_to_array(img)\n",
        "array=np.expand_dims(array,axis=0)\n",
        "img_tensor = array\n",
        "img_tensor /= 255.\n",
        "plt.imshow(img_tensor[0])\n",
        "plt.show()\n",
        "print(img_tensor.shape)\n",
        "\n",
        "model.predict(img_tensor)\n",
        "\n",
        "activations = activation_model.predict(img_tensor) \n",
        "last_layer_activation = activations[8]\n",
        "print(last_layer_activation.shape)\n",
        "\n",
        "heatmap = np.mean(last_layer_activation, axis=-1)\n",
        "heatmap = np.maximum(heatmap, 0) / np.max(heatmap)\n",
        "plt.imshow(heatmap[0])\n",
        "heatmap=heatmap[0,:,:,]\n",
        "\n",
        "eps = K.epsilon()\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    for k in range(len(last_layer_activation[0][i][j])):\n",
        "      if last_layer_activation[0][i][j][k]==0.:\n",
        "        last_layer_activation[0][i][j][k]=eps\n",
        "\n",
        "image_final=np.zeros_like(img_tensor)\n",
        "activations_final = activation_model.predict(image_final) \n",
        "\n",
        "last_layer_activation_final = activations_final[8]\n",
        "\n",
        "heatmap_final = np.mean(last_layer_activation_final, axis=-1)\n",
        "heatmap_final = np.maximum(heatmap_final, 0) / np.max(heatmap_final)\n",
        "plt.imshow(heatmap_final[0])\n",
        "\n",
        "weights_ratio=(last_layer_activation-last_layer_activation_final)/last_layer_activation\n",
        "\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    last_layer_activation[0][i][j]=(last_layer_activation[0][i][j]*weights_ratio[0][i][j])\n",
        "\n",
        "for i in range(len(last_layer_activation[0])):\n",
        "  for j in range(len(last_layer_activation[0][i])):\n",
        "    for k in range(len(last_layer_activation[0][i][j])):\n",
        "      if last_layer_activation[0][i][j][k]<0.:\n",
        "        last_layer_activation[0][i][j][k]=0.\n",
        "\n",
        "heatmap = np.mean(last_layer_activation, axis=-1)\n",
        "heatmap = np.maximum(heatmap, 0) / np.max(heatmap)\n",
        "plt.imshow(heatmap[0])\n",
        "plt.savefig(\"heatmap_car.jpg\")\n",
        "heatmap=heatmap[0,:,:,]\n",
        "\n",
        "img = keras.preprocessing.image.load_img('c.jpeg')\n",
        "img = keras.preprocessing.image.img_to_array(img)\n",
        "heatmap = np.uint8(255 * heatmap)\n",
        "jet = cm.get_cmap(\"jet\")\n",
        "jet_colors = jet(np.arange(256))[:, :3]\n",
        "jet_heatmap = jet_colors[heatmap]\n",
        "jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)\n",
        "jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n",
        "jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)\n",
        "superimposed_img = jet_heatmap * 0.7 + img\n",
        "superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)\n",
        "save_path = \"final_image_car.jpg\"\n",
        "superimposed_img.save(save_path)\n",
        "display(Image(save_path))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(1, 32, 32, 3)\n",
            "(1, 7, 7, 32)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_nzI2v1Y25e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}